Evaluating and Explaining Climate Science

Archive for the ‘Measurement’ Category

If we want to assess forecasts of floods, droughts and crop yields then we will need to know rainfall. We will also need to know temperature of course.

The forte of climate models is temperature. Rainfall is more problematic.

Before we get to model predictions about the future we need to review observations and the ability of models to reproduce them. Observations are also problematic – rainfall varies locally and over short durations. And historically we lacked effective observation systems in many locations and regions of the world, so data has to be pieced together and estimated from reanalysis.

Smith and his colleagues created a new rainfall dataset. Here is a comment from their 2012 paper:

Although many land regions have long precipitation records from gauges, there are spatial gaps in the sampling for undeveloped regions, areas with low populations, and over oceans. Since 1979 satellite data have been used to fill in those sampling gaps. Over longer periods gaps can only be filled using reconstructions or reanalyses..

Here are two views of the global precipitation data from a dataset which starts with the satellite era, that is, 1979 onwards – GPCP (Global Precipitation Climatology Project):

From Adler et al 2003

Figure 1

From Adler et al 2003

Figure 2

For historical data before satellites we only have rain gauge data. The GPCC dataset, explained in Becker et al 2013, shows the number of stations over time by region:

From Becker et al 2013

Figure 3- Click to expand

And the geographical distribution of rain gauge stations at different times:

From Becker et al 2013

Figure 4 – Click to expand

The IPCC compared the global trends over land from four different datasets over the last century and the last half-century:

From IPCC AR5 Ch. 2

Figure 5 – Click to expand

And the regional trends:

From IPCC AR5 Ch. 2

Figure 6 – Click to expand

The graphs for the annual change in rainfall, note the different scales for each region (as we would expect given the difference in average rainfall in different region):

From IPCC AR5 ch 2

Figure 7

We see that the decadal or half-decadal variation is much greater than any apparent long term trend. The trend data (as reviewed by the IPCC in figs 5 & 6) shows significant differences in the datasets but when we compare the time series it appears that the datasets match up better than indicated by the trend comparisons.

The data with the best historical coverage is 30ºN – 60ºN and the trend values for 1951-2000 (from different reconstructions) range from an annual increase of 1 to 1.5 mm/yr per decade (fig 6 / table 2.10 of IPCC report). This is against an absolute value of about 1000 mm/yr in this region (reading off the climatology in figure 2).

This is just me trying to put the trend data in perspective.

Models

Here is the IPCC AR5 chapter 9 on model comparisons to satellite-era rainfall observations. Top left is observations (basically the same dataset as figure 1 in this article over a slightly longer period with different colors) and bottom right is percentage error of model average with respect to observations:

From IPCC AR5 ch 9

Figure 8 – Click to expand

We can see that the average of all models has substantial errors on mean rainfall.

In Part Nine – Data I – Ts vs OLR we looked at the monthly surface temperature (“skin temperature”) from NCAR vs OLR measured by CERES. The slope of the data was about 2 W/m² per 1K surface temperature change. Commentators pointed out that this was really the seasonal relationship – it probably didn’t indicate anything further.

In Part Ten we looked at anomaly data: first where monthly means were removed; and then where daily means were removed. Mostly the data appeared to be a big noisy scatter plot with no slope. The main reason that I could see for this lack of relationship was that anomaly data didn’t “keep going” in one direction for more than a few days. So it’s perhaps unreasonable to expect that we would find any relationship, given that most circulation changes take time.

We haven’t yet looked at regional versions of Ts vs OLR, the main reason is I can’t yet see what we can usefully plot. A large amount of heat is exported from the tropics to the poles and so without being able to itemize the amount of heat lost from a tropical region or the amount of heat gained by a mid-latitude or polar region, what could we deduce? One solution is to look at the whole globe in totality – which is what we have done.

In this article we’ll look at the mean global annual data. We only have CERES data for complete years from 2001 to 2013 (data wasn’t available to end of the 2014 when I downloaded it).

Here are the time-series plots for surface temperature and OLR:

Figure 1

Here is the scatter plot of the above data, along with the best-fit linear interpolation:

Figure 2

The calculated slope is similar to the results we obtained from the monthly data (which probably showed the seasonal relationship). This is definitely the year to year data, but also gives us a slope that indicates positive feedback. The correlation is not strong, as indicated by the R² value of 0.37, but it exists.

As explained in previous posts, a change of 3.6 W/m² per 1K is a “no feedback” relationship, where a uniform 1K change in surface & atmospheric temperature causes an OLR increase of 3.6 W/m² due to increased surface and atmospheric radiation – a greater increase in OLR would be negative feedback and a smaller increase would be positive feedback (e.g. see Part Eight with the plot of OLR changes vs latitude and height, which integrated globally gives 3.6 W/m²).

The problem of the”no feedback” calculation is perhaps a bit more complicated and I want to dig into this calculation at some stage.

I haven’t looked at whether the result is sensitive to the date of the start of year. Next, I want to look at the changes in humidity, especially upper tropospheric water vapor, which is a key area for radiative changes. This will be a bit of work, because AIRS data comes in big files (there is a lot of data).

[I was going to post this new article not long after the last article in the series, but felt I was missing something important and needed to think about it. Instead I’ve not had any new insights and am posting for comment.]

In Part Nine – Data I, we looked at the relationship between Ts (surface temperature) and OLR (outgoing longwave radiation), for reasons all explained there.

The relationship shown there appears to be primarily the seasonal relationship, which looks like a positive feedback due to the 2W/m² per 1K temperature increase. What about the feedback on a different timescale from the seasonal relationship?

From the 2001-2013 data, here is the monthly mean and the daily mean for both Ts and OLR:

Figure 1

If we remove the monthly mean from the data, here are those same relationships (shown in the last article as anomalies from the overall 2001-2013 mean):

Figure 2 – Click to Expand

On a lag of 1 day there is a possible relationship with a low correlation – and the rest of the lags show no relationship at all.

Of course, we have created a problem with this new dataset – as the lag increases we are “jumping boundaries”. For example, on the 7-day lag all of the Ts data in the last week of April is being compared with the OLR data in the first week of May. With slowly rising temperatures, the last week of April will be “positive temperature data”, but the first week of May will be “negative OLR data”. So we expect 1/4 of our data to show the opposite relationship.

So we can show the data with the “monthly boundary jumps removed” – which means we can only show lags of say 1-14 days (with 3% – 50% of the data cut out); and we can also show the data as anomalies from the daily mean. Both have the potential to demonstrate the feedback on shorter timescales than the seasonal cycle.

First, here is the data with daily means removed:

Figure 3 – Click to Expand

Second, here is the data with the monthly means removed as in figure 2, but this time ensuring that no monthly boundaries are crossed (so some of the data is removed to ensure this):

Figure 4 – Click to Expand

So basically this demonstrates no correlation between change in daily global OLR and change in daily global temperature on less than seasonal timescales. (Or “operator error” with the creation of my anomaly data). This is excluding (because we haven’t tested it here) the very short timescale of day to night change.

This was surprising at first sight.

That is, we see global Ts increasing on a given day but we can’t distinguish any corresponding change in global OLR from random changes, at least until we get to seasonal time periods? (See graph in last article).

Then what is probably the reason came into view. Remember that this is anomaly data (daily global temperature with monthly mean subtracted). This bar graph demonstrates that when we are looking at anomaly data, most of the changes in global Ts are reversed the next day, or usually within a few days:

Figure 5

This means that we are unlikely to see changes in Ts causing noticeable changes in OLR unless the climate response we are looking for (humidity and cloud changes) occurs within a day or two.

That’s my preliminary thinking, looking at the data – i.e., we can’t expect to see much of a relationship, and we don’t see any relationship.

One further point – explained in much more detail in the (short) series Measuring Climate Sensitivity – is that of course changes in temperature are not caused by some mechanism that is independent of radiative forcing.

That is, our measurement problem is compounded by changes in temperature being first caused by fluctuations in radiative forcing (the radiation balance) and ocean heat changes and then we are measuring the “resulting” change in the radiation balance resulting from this temperature change:

Part Six – Nonlinearity and Dry Atmospheres – demonstrating that different distributions of water vapor yet with the same mean can result in different radiation to space, and how this is important for drier regions like the sub-tropics

In the last article we looked at a paper which tried to unravel – for clear sky only – how the OLR (outgoing longwave radiation) changed with surface temperature. It did the comparison by region, by season and from year to year.

The key point for new readers to understand – why are we interested in how OLR changes with surface temperature? The concept is not so difficult. The practical analysis presents more problems.

Let’s review the concept – and for more background please read at least the start of the last article: if we increase the surface temperature, perhaps due to increases in GHGs, but it could be due to any reason, what happens to outgoing longwave radiation? Obviously, we expect OLR to increase. The real question is how by how much?

If there is no feedback then OLR should increase by about 3.6 W/m² for every 1K in surface temperature (these values are global averages):

If there is positive feedback, perhaps due to more humidity, then we expect OLR to increase by less than 3.6 W/m² – think “not enough heat got out to get things back to normal”

If there is negative feedback, then we expect OLR to increase by more than 3.6 W/m². In the paper we reviewed in the last article the authors found about 2 W/m² per 1K increase – a positive feedback, but were only considering clear sky areas

One reader asked about an outlier point on the regression slope and whether it affected the result. This motivated me to do something I have had on my list for a while now – get “all of the data” and analyse it. This way, we can review it and answer questions ourselves – like in the Visualizing Atmospheric Radiation series where we created an atmospheric radiation model (first principles physics) and used the detailed line by line absorption data from the HITRAN database to calculate how this change and that change affected the surface downward radiation (“back radiation”) and the top of atmosphere OLR.

With the raw surface temperature, OLR and humidity data “in hand” we can ask whatever questions we like and answer these questions ourselves..

NCAR reanalysis, CERES and AIRS

AIRS has a “hyper-spectral” instrument, which means it looks at lots of frequency channels. The intensity of radiation at these many wavelengths can be converted, via calculation, into measurements of atmospheric temperature at different heights, water vapor concentration at different heights, CO2 concentration, and concentration of various other GHGs. Additionally, AIRS calculates total OLR (it doesn’t measure it – i.e. it doesn’t have a measurement device from 4μm – 100μm). It also measures parameters like “skin temperature” in some locations and calculates the same in other locations.

For the purposes of this article, I haven’t yet dug into the “how” and the reliability of surface AIRS measurements. The main point to note about satellites is they sit at the “top of atmosphere” and their ability to measure stuff near the surface depends on clever ideas and is often subverted by factors including clouds and surface emissivity. (AIRS has microwave instruments specifically to independently measure surface temperature even in cloudy conditions, because of this problem).

NCAR is a “reanalysis product”. It is not measurement, but it is “informed by measurement”. It is part measurement, part model. Where there is reliable data measurement over a good portion of the globe the reanalysis is usually pretty reliable – only being suspect at the times when new measurement systems come on line (so trends/comparisons over long time periods are problematic). Where there is little reliable measurement the reanalysis depends on the model (using other parameters to allow calculation of the missing parameters).

Some more explanation in Water Vapor Trends under the sub-heading Reanalysis – or Filling in the Blanks.

For surface temperature measurements reanalysis is not subverted by models too much. However, the mainstream surface temperature series are surely better than NCAR – I know that there is an army of “climate interested people” who follow this subject very closely. (I am not in that group).

I used NCAR because it is simple to download and extract. And I expect – but haven’t yet verified – that it will be quite close to the various mainstream surface temperature series. If someone is interested and can provide daily global temperature from another surface temperature series as an Excel, csv, .nc – or pretty much any data format – we can run the same analysis.

For those interested, see note 1 on accessing the data.

Results – Global Averages

For our starting point in this article I decided to look at global averages from 2001 to 2013 inclusive (data from CERES not yet available for the whole of 2014). This was after:

looking at daily AIRS data

creating and comparing NCAR over 8 days with AIRS 8-day averages for surface skin temperature and surface air temperature

creating and comparing AIRS over 8-days with CERES for TOA OLR

More on those points in later articles.

The global relationship with surface temperature and OLR is what we have a primary interest in – for the purpose of determining feedbacks. Then we want to figure out some detail about why it occurs. I am especially interested in the AIRS data because it is the only global measurement of upper tropospheric water vapor (UTWV) – and UTWV along with clouds are the key factors in the question of feedback – how OLR changes with surface temperature. For now, we will look at the simple relationship between surface temperature (“skin temperature”) and OLR.

Here is the data, shown as an anomaly from the global mean values over the period Jan 1st, 2001 to Dec 31st, 2013. Each graph represents a different lag – how does global OLR (CERES) change with global surface temperature (NCAR) on a lag of 1 day, 7 days, 14 days and so on:

Figure 1 – Click to Expand

The slope gives the “apparent feedback” and the R² simply reflects how much of the graph is explained by the linear trend. This last value is easily estimated just by looking at each graph.

For reference, here is the timeseries data, as anomalies, with the temperature anomaly multiplied by a factor of 3 so its magnitude is similar to the OLR anomaly:

Figure 2 – Click to Expand

Note on the calculation – I used the daily data to calculate a global mean value (area-weighted) and calculated one mean value over the whole time period then subtracted it from every daily data value to obtain an anomaly for each day. Obviously we would get the same slope and R² without using anomaly data (just a different intercept on the axes).

For reference, mean OLR = 238.9 W/m², mean Ts = 288.0 K.

My first question – before even producing the graphs – was whether a lag graph shows the change in OLR due to a change in Ts or due to a mixture of many effects. That is, what is the interpretation of the graphs?

The second question – what is the “right lag” to use? We don’t expect an instant response when we are looking for feedbacks:

The OLR through the window region will of course respond instantly to surface temperature change

The OLR as a result of changing humidity will depend upon how long it takes for more evaporated surface water to move into the mid- to upper-troposphere

The OLR as a result of changing atmospheric temperature, in turn caused by changing surface temperature, will depend upon the mixture of convection and radiative cooling

To say we know the right answer in advance pre-supposes that we fully understand atmospheric dynamics. This is the question we are asking, so we can’t pre-suppose anything. But at least we can suggest that something in the realm of a few days to a few months is the most likely candidate for a reasonable lag.

But the idea that there is one constant feedback and one constant lag is an idea that might well be fatally flawed, despite being seductively simple. (A little more on that in note 3).

And that is one of the problems of this topic. Non-linear dynamics means non-linear results – a subject I find hard to describe in simple words. But let’s say – changes in OLR from changes in surface temperature might be “spread over” multiple time scales and be different at different times. (I have half-written an article trying to explain this idea in words, hopefully more on that sometime soon).

But for the purpose of this article I only wanted to present the simple results – for discussion and for more analysis to follow in subsequent articles.

Articles in the Series

Part One – introducing some ideas from Ramanathan from ERBE 1985 – 1989 results

Part Six – Nonlinearity and Dry Atmospheres – demonstrating that different distributions of water vapor yet with the same mean can result in different radiation to space, and how this is important for drier regions like the sub-tropics

Notes

Note 1: Boring Detail about Extracting Data

On the plus side, unlike many science journals, the data is freely available. Credit to the organizations that manage this data for their efforts in this regard, which includes visualization software and various ways of extracting data from their sites. However, you can still expect to spend a lot of time figuring out what files you want, where they are, downloading them, and then extracting the data from them. (Many traps for the unwary).

You get lat, long, and time in the file as well as the parameter. Care needed to navigate to the right folder because the filenames are the same for the 4x daily and the daily data.

NCAR are using latest version .nc files (which Matlab circa 2010 would not open, I had to update to the latest version – many hours wasted trying to work out the reason for failure).

CERES – data in .nc files, you select the data you want and the time period but it has to be a less than 2G file and you get a file to download. I downloaded daily OLR data for each annual period. Data in a 1ºx 1º grid. CERES are using older version .nc so there should be no problem opening.

AIRS – data in .hdf files, in daily, 8-day average, or monthly average. The data is “ascending” = daytime, “descending” = nighttime plus some other products. Daily data doesn’t give global coverage (some gaps). 8-day average does but there are some missing values due to quality issues. Data in a 1ºx 1º grid. I used v6 data.

HDF is not trivial to open up. The AIRS team have helpfully provided a Matlab tool to extract data which helped me. I think I still spent many hours figuring out how to extract what I needed.

Files Sizes – it’s a lot of data:

NCAR files that I downloaded (skin temperature) are only 12MB per annual file.

CERES files with only 2 parameters are 190MB per annual file.

AIRS files as 8-day averages (or daily data) are 400MB per file.

Also the grid for each is different. Lat from S-pole to N-pole in CERES, the reverse for AIRS and NCAR. Long from 0.5º to 359.5º in CERES but -179.5 to 179.5 in AIRS. (Note for any Matlab people, it won’t regrid, say using interp2, unless the grid runs from lowest number to highest number).

Note 2: Checking data – because I plan on using the daily 1ºx1º grid data from CERES and NCAR, I used it to create the daily global averages. As a check I downloaded the global monthly averages from CERES and compared. There is a discrepancy, which averages at 0.1 W/m².

Here is the difference by month:

Figure 3 – Click to expand

And a scatter plot by month of year, showing some systematic bias:

Figure 4

As yet, I haven’t dug any deeper to find if this is documented – for example, is there a correction applied to the daily data product in monthly means? is there an issue with the daily data? or, more likely, have I %&^ed up somewhere?

The relationship between global-mean radiative forcing and global-mean climate response (temperature) is of intrinsic interest in its own right. A number of recent studies, for example, discuss some of the broad limitations of (1) and describe procedures for using it to estimate Q from GCM experiments (Hansen et al. 1997; Joshi et al. 2003; Gregory et al. 2004) and even procedures for estimating from observations (Gregory et al. 2002).

While we cannot necessarily dismiss the value of (1) and related interpretation out of hand, the global response, as will become apparent in section 9, is the accumulated result of complex regional responses that appear to be controlled by more local-scale processes that vary in space and time.

If we are to assume gross time–space averages to represent the effects of these processes, then the assumptions inherent to (1) certainly require a much more careful level of justification than has been given. At this time it is unclear as to the specific value of a global-mean sensitivity as a measure of feedback other than providing a compact and convenient measure of model-to-model differences to a fixed climate forcing (e.g., Fig. 1).

[Emphasis added and where the reference to “(1)” is to the linear relationship between global temperature and global radiation].

If, for example, λ is actually a function of location, season & phase of ENSO.. then clearly measuring overall climate response is a more difficult challenge.

In Part Seven we had a look at a 2008 paper by Gettelman & Fu which assessed models vs measurements for water vapor in the upper troposphere.

In this article we will look at a 2010 paper by Chung, Yeomans & Soden. This paper studies outgoing longwave radiation (OLR) vs temperature change, for clear skies only, in three ways (and comparing models and measurements):

by region

by season

year to year

Why is this important and what is the approach all about?

Let’s suppose that the surface temperature increases for some reason. What happens to the total annual radiation emitted by the climate system? We expect it to increase. The hotter objects are the more they radiate.

If there is no positive feedback in the climate system then for a uniform global 1K (=1ºC) increase in surface & atmospheric temperature we expect the OLR to increase by 3.6 W/m². This is often called, by convention only, the “Planck feedback”. It refers to the fact that an increased surface temperature, and increased atmospheric temperature, will radiate more – and the “no feedback value” is 3.6 W/m² per 1K rise in temperature.

To explain a little further for newcomers.. with the concept of “no positive feedback” an initial 1K surface temperature rise – from any given cause – will stay at 1K. But if there is positive feedback in the climate system, an initial 1K surface temperature rise will result in a final temperature higher than 1K.

If the OLR increases by less than 3.6 W/m² the final temperature will end up higher than 1K – positive feedback. If the OLR increases by more than 3.6 W/m² the final temperature will end up lower than 1K – negative feedback.

Base Case

At the start of their paper they show the calculated clear-sky OLR change as the result of an ideal case. This is the change in OLR as a result of the surface and atmosphere increasing uniformly by 1K:

first, from the temperature change alone

second, from the change in water vapor as a result of this temperature change, assuming relative humidity stays constant

finally, from the first and second combined

From Chung et al (2010)

Figure 1 – Click to expand

The graphs show the breakdown by pressure (=height) and latitude. 1000mbar is the surface and 200mbar is approximately the tropopause, the place where convection stops.

The sum of the first graph (note 1) is the “no feedback” response and equals 3.6 W/m². The sum of the second graph is the “feedback from water vapor” and equals -1.6 W/m². The combined result in the third graph equals 2.0 W/m². The second and third graphs are the result if relative humidity is constant.

We can also see that the tropics is where most of the changes take place.

They say:

One striking feature of the fixed-RH kernel is the small values in the tropical upper troposphere, where the positive OLR response to a temperature increase is offset by negative responses to the corresponding vapor increase. Thus under a constant RH- warming scenario, the tropical upper troposphere is in a runaway greenhouse state – the stabilizing effect of atmospheric warming is neutralized by the increased absorption from water vapor. Of course, the tropical upper troposphere is not isolated but is closely tied to the lower tropical troposphere where the combined temperature-water vapor responses are safely stabilizing.

To understand the first part of their statement, if temperatures increase and overall OLR does not increase at all then there is nothing to stop temperatures increasing. Of course, in practice, the “close to zero” increase in OLR for the tropical upper troposphere under a temperature rise can’t lead to any kind of runaway temperature increase. This is because there is a relationship between the temperatures in the upper troposphere and the lower- & mid- troposphere.

Relative Humidity Stays Constant?

Back in 1967, Manabe & Wetherald published their seminal paper which showed the result of increases in CO2 under two cases – with absolute humidity constant and with relative humidity constant:

Generally speaking, the sensitivity of the surface equilibrium temperature upon the change of various factors such as solar constant, cloudiness, surface albedo, and CO2 content are almost twice as much for the atmosphere with a given distribution of relative humidity as for that with a given distribution of absolute humidity..

..Doubling the existing CO2 content of the atmosphere has the effect of increasing the surface temperature by about 2.3ºC for the atmosphere with the realistic distribution of relative humidity and by about 1.3ºC for that with the realistic distribution of absolute humidity.

They explain important thinking about this topic:

Figure 1 shows the distribution of relative humidity as a function of latitude and height for summer and winter. According to this figure, the zonal mean distributions of relative humidity closely resemble one another, whereas those of absolute humidity do not. These data suggest that, given sufficient time, the atmosphere tends to restore a certain climatological distribution of relative humidity responding to the change of temperature.

It doesn’t mean that anyone should assume that relative humidity stays constant under a warmer world. It’s just likely to be a more realistic starting point than assuming that absolute humidity stays constant.

I only point this out for readers to understand that this idea is something that has seemed reasonable for almost 50 years. Of course, we have to question this “reasonable” assumption. How relative humidity changes as the climate warms or cools is a key factor in determining the water feedback and, therefore, it has had a lot of attention.

Results From the Paper

The observed rates of radiative damping from regional, seasonal, and interannual variations are substantially smaller than the rate of Planck radiative damping (3.6W/m²), yet slightly larger than that anticipated from a uniform warming, constant-RH response (2.0 W/m²).

The three comparison regressions can be seen, with ERBE data on the left and model results on the right:

From Chung et al (2010)

Figure 2 – Click to expand

In the next figure, the differences between the models can be seen, and compared with ERBE and CERES results. The red “Planck” line is the no-feedback line, showing that (for these sets of results) models and experimental data show a positive feedback (when looking at clear sky OLR).

From Chung et al (2010)

Figure 3 – Click to expand

Conclusion

At the least, we can see that climate models and measured values are quite close, when the results are aggregated. Both the model and the measured results are a long way from neutral feedback (the dashed slope in figure 2 and the red line in figure 3), instead they show positive feedback, quite close to what we would expect from constant relative humidity. The results indicate that relative humidity declines a little in the warmer case. The results also indicate that the models calculate a little more positive feedback than the real world measurements under these cases.

What does this mean for feedback from warming from increased GHGs? It’s the important question. We could say that the results tell us nothing, because how the world warms from increasing CO2 (and other GHGs) will change climate patterns and so seasonal, regional and year to year changes in periods from 1985-1988 and 2005-2008 are not particularly useful.

We could say that the results tell us that water vapor feedback is demonstrated to be a positive feedback, and matches quite closely the results of models. Or we could say that without cloudy sky data the results aren’t very interesting.

At the very least we can see that for current climate conditions under clear skies the change in OLR as temperature changes indicates an overall positive feedback, quite close to constant relative humidity results and quite close to what models calculate.

The ERBE results include the effect of a large El Nino and I do question whether year to year changes (graph c in figs 2 & 3) under El Nino to La Nino changes can be considered to represent how the climate might warm with more CO2. If we consider how the weather patterns shift during El-Nino to La Nina it has long been clear that there are positive feedbacks, but also the weather patterns end up back to normal (the cycle ends). I welcome knowledgeable readers explaining why El Nino feedback patters are relevant to future climate shifts, perhaps this will help me to clarify my thinking, or correct my misconceptions.

However, the CERES results from 2005-2008 don’t include the effect of a large El Nino and they show an overall slightly more positive feedback.

I asked Brian Soden a few question about this paper and he was kind enough to respond:

Q. Given the much better quality data since CERES and AIRS, why is ERBE data the focus?
A. At the time, the ERBE data was the only measurement that covered a large ENSO cycle (87/88 El Nino event followed by 88/89 La Nina)

Q. Why not include cloudy skies as well in this review? Collecting surface temperature data is more challenging of course because it needs a different data source. Is there a comparable study that you know of for cloudy skies?
A. The response of clouds to surface temperature changes is more complicated. We wanted to start with something relatively simple; i.e., water vapor. Andrew Dessler at Texas AM has a paper that came out a few years back that looks at total-sky fluxes and thus includes the effects on clouds.

Q. Do you know of any studies which have done similar work with what must now be over 10 years of CERES/AIRS.
A. Not off-hand. But it would be useful to do.

Articles in the Series

Part One – introducing some ideas from Ramanathan from ERBE 1985 – 1989 results

Part Six – Nonlinearity and Dry Atmospheres – demonstrating that different distributions of water vapor yet with the same mean can result in different radiation to space, and how this is important for drier regions like the sub-tropics

Notes

Note 1: The values are per 100 mbar “slice” of the atmosphere. So if we want to calculate the total change we need to sum the values in each vertical slice, and of course, because they vary through latitude we need to average the values (area-weighted) across all latitudes.

We compared the rate of change of ice volume – as measured in the Huybers 2007 dataset – with summer insolation at 65ºN. The results were interesting, the results correlated very well for the first 200 kyrs, then drifted out of phase. As a result the (Pearson) correlation over 500 kyrs was very low, but quite decent for the first 200 kyrs.

Without any further data we might assume that the results demonstrated that the dataset without “orbital tuning” – and a lack of objective radiometric dating – was drifting away from reality as time went on, and an “orbitally tuned” dataset was the best approach. We would definitely expect that older dates have more uncertainty, as errors accumulate when we use any kind of model for time vs depth.

However, in an earlier article we looked at more objective dates for Termination II (and also in the comments, at some earlier terminations). These dates were obtained via radiometric dating from a variety of locations and methods.

So I wondered:

What happens if we take a dataset like Huybers 2007 and “remap” it using agemarkers?

This is basically how most of the ice core datasets are constructed, although the methods are more sophisticated (see note 1).

For my rough and ready approach I simply provided a set of termination dates (halfway point of ice volume from peak glacial to peak interglacial) from both Huybers and from Winograd et al 1992. Then I remapped the timebase for the existing Huybers proxy data between each set of agemarkers.

It’s probably easier to show the before and after comparison, rather than explain the method further. Note the low point between 100 and 150 kyrs BP. This corresponds to less ice, it is the interglacial:

Figure 1

The method is basically a linear remapping. I’m sure there are better ways, but I don’t expect they would have a material impact on the outcome.

One point that’s important (with my very simple method) is the oldest agemarker we consider can cause an inconsistency (as there is nothing to constrain the dates between the last agemarker and the end date), which is why the first set below uses 270 kyrs.

T- III is dated by Winograd 1992 at 253 kyrs. So I picked a date shortly after that.

Here is the comparison of rate of change of ice volume with insolation, with the same conventions as in the last article. We can see that everything is nicely anti-correlated:

Figure 2 – Click to Expand

For comparison, the result (in the last article) from 0-200 kyrs BP without remapping the proxy dataset. We can see that everything is nicely correlated:

Figure 3 – Click to Expand

For the remapped data: correlation = -0.30. This is as negatively correlated to the insolation value as LR04 (an “orbitally-tuned” dataset) is positively correlated.

For interest I did the same exercise with a 0 – 200kyr BP timebase. This means everything from 140 kyrs – 200 yrs was not constrained by a revised T-III date. The result: correlation = 0. The interpretation is simple – the older data is not pulled out of alignment due to a later objective T-III date, so there is a better match of insolation with rate of change of ice volume for this older data.

Conclusion

Is there a conclusion? It’s surely staring us in the face so is left as an exercise for the interested student.

Twelve – GCM V – Ice Age Termination – very recent work from He et al 2013, using a high resolution GCM (CCSM3) to analyze the end of the last ice age and the complex link between Antarctic and Greenland

Thirteen – Terminator II – looking at the date of Termination II, the end of the penultimate ice age – and implications for the cause of Termination II

Fourteen – Concepts & HD Data – getting a conceptual feel for the impacts of obliquity and precession, and some ice age datasets in high resolution

Notes

Note 1:

Here is an extract from Parennin et al 2007, The EDC3 chronology for the EPICA Dome C ice core:

In this article, we present EDC3, the new 800 kyr age scale of the EPICA Dome C ice core, which is generated using a combination of various age markers and a glaciological model. It is constructed in three steps.

First, an age scale is created by applying an ice flow model at Dome C. Independent age markers are used to control several poorly known parameters of this model (such as the conditions at the base of the glacier), through an inverse method.

Second, the age scale is synchronised onto the new Greenlandic GICC05 age scale over three time periods: the last 6 kyr, the last deglaciation, and the Laschamp event (around 41 kyr BP).

Third, the age scale is corrected in the bottom ∼500 m (corresponding to the time period 400–800 kyr BP), where the model is unable to capture the complex ice flow pattern..

Roe’s paper appears to show an excellent match between the rate of change of ice volume and insolation at 65°N in June. I’ve been puzzled by the paper for a while, because if this value of insolation does successfully predict changes in ice volume then case closed. Except we struggle to match glacial terminations with insolation (see earlier posts like Part Thirteen, Twelve, Eleven – End of the Last Ice age).

And we should also expect to find a 100 kyr period in the 65°N insolation spectrum. But we don’t.

To be fair to Roe, he does state:

The Milankovitch hypothesis as formulated here does not explain the large rapid deglaciations that occurred at the end of some of the ice age cycles

[Emphasis added].

To be critical, it doesn’t seem like anyone is disputing that ice sheets wax and wane with at least some attachment to 40k (obliquity) and 20k (precession) cycles so what exactly does the paper demonstrate that is new? The missing bit of the puzzle is why ice ages start and end.

One of the reasons I’ve spent quite a bit of time collecting and understanding datasets – see Part Fourteen – Concepts & HD Data – was for this kind of problem. Roe’s figure 2 spans half a page but covers 800,000 years. With the thick lines used I can’t actually tell if there is a match, and being poor at real statistics I want to see the data rather than just accept a correlation.

There’s not much point comparing SPECMAP (or LR04) with insolation because both of these datasets are “tuned” to summer 65°N insolation. If we find success then we accept that the producers of the dataset were competent in their objective. If we find lack of success we have to write to them with bad news. No one wants to do that.

Fortunately we have an interesting dataset from Peter Huybers (2007). This is an update of HW04 (Huybers & Wunsch 2004) which created a proxy for global ice volume from deep ocean cores without “orbital tuning”. It’s based on an autocorrelated sedimentation model, requiring that key turning points from many different cores all occur at the same time, and a key dateable event at around 800,000 years ago that shows up in most cores.

Some readers are wondering:

Why not use the ice cores you have been writing about?

Good question. The oxygen isotope (δ18O), or deuterium isotope (δD), in the ice is more a measure of local temperature than anything else (and it’s complicated). So Greenland and Antarctic ice cores provide lots of useful data, but not global ice volume. For that, we need to capture the δ18O stored in deep ocean sediments. The δ18O in deep ocean cores, to a first order, appears to be a measure of the amount of water locked up in global ice sheets. However, we have no easy way to objectively date the ocean cores, so some assumptions are needed.

Fortunately, Roe compared his theory with two datasets, the famous SPECMAP (warning, orbital tuning was used in the creation of this dataset) and HW04:

Figure 1

I downloaded the updated Huybers 2007 dataset. It is in 1 kyr intervals. I have calculated the insolation at all latitudes and all days for the last 500 kyrs using Jonathan Levine’s MATLAB program. This is also in 1 kyrs intervals. I used the values at 65N and June 21st (day 172 – thanks Climateer, for helping me with the basics of calendar days!).

I calculated change in ice volume in a very simple way – (value at time t+1 – value at time t) divided by time change. I scaled the resulting dataset to the same range as the insolation anomalies – so that they plot nicely. And I plotted insolation anomaly = mean(insolation) – insolation:

Figure 2 – Click to Expand

The two sets of data look very similar over the last 500 kyrs. I assume that some minor changes, e.g., at about 370 kyrs, are due to dataset updates. Note that insolation anomaly is effectively inverted to help match trends by eye – high insolation should lead to negative change in ice volume and vice-versa.

For reference, here is my calculation on its own (click to get the large version):

Figure 3 – Click to Expand

I did a Pearson correlation between the two datasets and obtained 0.08. That is, very little correlation. This just tells us what we can see from looking at the graph – the two key values are in phase to begin with then move out of phase and back into phase by the end.

I’m a bit of a statistics amateur so comparing datasets except by looking is not my forte. Perhaps a rookie mistake somewhere.

Then I checked lag correlations. The physical reasoning is that deep ocean concentration of 18O will take a few thousand years at least to respond to ice volume changes, simply due to the slow circulation of the major ocean currents. The results show there is a better correlation with a lag of 35,000 years, but there is no physical reason for this, it is probably just a better fit to a dataset with an apparent slow phase drift across the period of record. At a meaningful large ocean current lag of a few thousand years the correlation is worse (anti-correlated):

Figure 4

On the plus side, the first 200 kyrs look quite impressive, including terminations:

Figure 5

Figure 6

This has got me wondering.

What do we notice from the data for the first 200 kyrs (figure 6)? Well, the last two terminations (check out the last few posts) are easily identified because the rate of change of ice volume in proportion to insolation is about four times its value when no termination takes place.

Forgetting about the small problem of the Southern Hemisphere lead in the last deglaciation (Part Eleven – End of the Last Ice age), there is something interesting going on here. Almost like a theory that is just missing one easily identified link, one piece of the jigsaw puzzle that just needs to be fitted in, and the new Nature paper is waiting..

Onto some details.. it seems that T-II, if marked by the various radiometric dating values we saw Part Thirteen – Terminator II, would cause the 100k-200k values to move out of phase (the big black dip at about 125 kyrs would move about 15 kyrs to the left). So my next objective (see Sixteen – Roe vs Huybers II) is to set an age marker for Termination II from the radiometric dating values and “slide” the Huybers 2007 dataset to this and the current T1 dating. Also, the ice core proxies recorded in deep ocean cores must lag real ice volume changes by some period like say 1 – 3 kyrs (see note 1). This helps the Roe hypothesis because the black curves move to the left.

Let’s see what happens with these changes.

And hopefully, sharp-eyed readers are going to identify opportunities for improvement in this article, as well as the missing piece of the puzzle that will lead to the coveted Nature paper..

Twelve – GCM V – Ice Age Termination – very recent work from He et al 2013, using a high resolution GCM (CCSM3) to analyze the end of the last ice age and the complex link between Antarctic and Greenland

Thirteen – Terminator II – looking at the date of Termination II, the end of the penultimate ice age – and implications for the cause of Termination II

Fourteen – Concepts & HD Data – getting a conceptual feel for the impacts of obliquity and precession, and some ice age datasets in high resolution

Insolation data calculated from Jonathan Levine’s MATLAB program (just ask for this data in Excel or MATLAB format)

Notes

Note 1: See, for example, Wunsch & Heimbach 2008:

The various time scales for distribution of tracers and proxies in the global ocean are critical to the interpretation of data from deep- sea cores. To obtain some basic physical insight into their behavior, a global ocean circulation model, forced to least-square consistency with modern data, is used to find lower bounds for the time taken by surface-injected passive tracers to reach equilibrium. Depending upon the geographical scope of the injection, major gradients exist, laterally, between the abyssal North Atlantic and North Pacific, and vertically over much of the ocean, persisting for periods longer than 2000 years and with magnitudes bearing little or no relation to radiocarbon ages. The relative vigor of the North Atlantic convective process means that tracer events originating far from that location at the sea surface will tend to display abyssal signatures there first, possibly leading to misinterpretation of the event location. Ice volume (glacio-eustatic) corrections to deep-sea d18O values, involving fresh water addition or subtraction, regionally at the sea surface, cannot be assumed to be close to instantaneous in the global ocean, and must be determined quantitatively by modelling the flow and by including numerous more complex dynamical interactions.